1. Field of the Invention
The present invention relates generally to the field of early detection and diagnosis of incipient machine failure or process upset. More particularly, the invention is directed to generating appropriate alerts on the basis of residual signals indicative of system behavior.
2. Brief Description of the Related Art
A variety of new and advanced techniques have emerged in industrial process control, machine control, system surveillance, and condition based monitoring to address drawbacks of traditional sensor-threshold-based control and alarms. The traditional techniques did little more than provide responses to gross changes in individual metrics of a process or machine, often failing to provide adequate warning to prevent unexpected shutdowns, equipment damage, loss of product quality or catastrophic safety hazards.
According to one branch of the new techniques, empirical models of the monitored process or machine are used in failure detection and in control. Such models effectively leverage an aggregate view of surveillance sensor data to achieve much earlier incipient failure detection and finer process control. By modeling the many sensors on a process or machine simultaneously and in view of one another, the surveillance system can provide more information about how each sensor (and its measured parameter) ought to behave. Additionally, these approaches have the advantage that no additional instrumentation is typically needed, and sensors in place on the process or machine can be used.
An example of such an empirical surveillance system is described in U.S. Pat. No. 5,764,509 to Gross et al., the teachings of which are incorporated herein by reference. Therein is described an empirical model using a similarity operator against a reference library of known states of the monitored process, and an estimation engine for generating estimates of current process states based on the similarity operation, coupled with a sensitive statistical hypothesis test to determine if the current process state is a normal or abnormal state. The role of the similarity operator in the above empirical surveillance system is to determine a metric of the similarity of a current set of sensor readings to any of the snapshots of sensor readings contained in the reference library. The similarity metric thusly rendered is used to generate an estimate of what the sensor readings ought to be, from a weighted composite of the reference library snapshots. The estimate can then be compared to the current readings for monitoring differences indicating incipient process upset, sensor failure or the like.
Early detection of sensor failure, process upset or machine fault are afforded in such monitoring systems by sensitive statistical tests such as the sequential probability ratio test, also described in the aforementioned patent to Gross et al. The result of such a test when applied to the residual of the difference of the actual sensor signal and estimated sensor signal, is a decision as to whether the actual and estimate signals are the same or different, with user-selectable statistical confidence.
Successful application of the sequential probability ratio test to the empirical model-generated residuals of the above systems is contingent on several assumptions regarding those residuals. First, it is assumed that if the monitored system is behaving correctly, then the only differences between the actual sensor value and the estimated value are a function of noise. For empirical models that generate estimates, this is often a function of the quality of the data that was available to train the model. Second, it is assumed that this noise is both identically distributed and Gaussian, as well as temporally independent. Unfortunately, this is not always the case in many applications of the empirical monitoring systems mentioned above.
Because of these issues, an implementation of an empirical model according to a similarity operator, coupled to a SPRT alert generation module can result in nuisance alerts, especially if the model is set up with limited training data for the eventual expected range of operation of the process or machine being monitored. It is desirable to have alternative mechanisms for generating alerts on the basis of the comparison of the actual raw sensor data to the sensor data estimated by the similarity operator empirical model.